$CSPX (+0,52%)
$IWDA (+0,44%)
$EQQQ (+0,27%)

The calculation is the country's trade deficit with the USA divided by the country's exports to the USA

Ex:

Vietnam: exports 136.6, imports 13.1

Deficit = 123.5

123,5/136,6 = 90 %

I am currently saving the $IWDA (+0,44%) and the $EIMI (-0,28%)and a little bit of $BTC (+0,9%) .

As I am still young (20) I would like to buy another yield booster.

As the prices are also relatively attractive at the moment, I was thinking of something like the $EQQQ (+0,27%)

What do you think? Would you rather expand the Core $IWDA (+0,44%) further, or add a Nasdaq?

Good evening GQ,

Today I would like to introduce you to my Nasdaq100 Leveraged Long-Term Trading System. The whole thing is by and large an AI model which is based on the $EQQQ (+0,27%) the $TQQQ , $SQQQ or cash.

Origin

The idea for the model was born in December after a conversation with @Epi about his GTAA model which I have been following for some time. The first idea at that time was to buy the leveraged Nasdaq100 by comparing the 50-SMA and 200-SMA. $TQQQ to buy the leveraged one. After various backtests on portfolio visualizers and unsuccessful attempts in Excel to find a pattern within the technical indicators, the idea of my own AI model was born.

The model

The LTTS model basically consists of 2 parts in Python. The first part uses an ensemble of 3 AI models to forecast the course of the Nasdaq100 over the next few days using various technical indicators. The history of SMA, RSI, MACD, ROC, VIX or various volume parameters back to 2010 are used. The output of the ensemble is a trading signal and the current model confidence.

The second part of the model consists of a kind of optimization loop that uses the Bayesian optimization model to try to optimize several indicators such as the buy thresholds for $TQQQ , $SQQQ , cash, smoothing parameters, interaction of trading signal with confidence, ensemble agreement and other parameters through backtests. This means that the model itself carries out backtests with the signals from the forecast model over various time ranges and settings in order to optimize performance. The AI is rewarded with high returns or low drawdowns, while deductions are made for an excessively high number of trades (long term model) or low sharp ratios.

Development

When creating the concept, it quickly became clear that the model should run in Python on Google Colab. Since I myself work as an engineer in the development department of a semiconductor company, I am basically familiar with models and programming, but in much lower-level programming languages. Since my Python knowledge is limited, I decided to develop the model with the help of LLM-AI models (mainly Claude Sonnet 3.5, individually also ChatGPT and Perplex) and to optimize the code. The experience of using AI on a daily basis at work has been confirmed time and again. The AI has suggested code that I would never have managed to program in this way, but you have to check each line individually and debug very carefully whether everything has been understood correctly and the code does what it is supposed to do. In total, about 80 hours have gone into the development in the last few months, the final training of the models (on my iMac from 2012 - Colab has a limited runtime) is about 28 hours and will be repeated every 2-3 weeks. The daily calculation of the current signals has a runtime of approx. 1 minute on Colab.

Outlook

Even though the backtests have produced very good results, the model still has to prove itself, which is why there is currently still a small investment amount in the new GQ portfolio. I will share my experiences with the model with you over the next few months. In theory, the model can be trained (and traded) with any asset, but currently only the Nasdaq with the corresponding levers is planned.

Current LTTS signal: Cash (will be entered accordingly in the GQ portfolio in order to be able to compare with the market)

Best regards

Your Internet Explorer

"Welt" has analyzed global capital flows together with the data provider Bloomberg. Findings (unsurprisingly): Investors continue to rely on "American exceptionalism". The USA remains the most important market for investors.

The largest capital inflows were received (Top2):

• $VUAG (+0,54%) Vanguard S&P 500 - approx. EUR +20 bn
• $EQQQ (+0,27%) Invesco QQQ - over EUR +3 billion

Investors are not only focusing on tech, but also on financial stocks. Obviously with the expectation that Trump will further deregulate and loosen merger control will boost the M&A business.

The analysis clearly shows that fund investors will continue to focus on US equities, tech and financials in 2025. European ETFs play only a minor role, as do ETFs with stocks from the Middle Kingdom.

(Source: Welt, 30.01.)

Now that the new year has started, I have gathered motivation to clean up/adjust my portfolio a bit.

I have liquidated some positions and chosen the $VWRL (+0,84%) as my core ETF. I would like to expand this further so that around 60-70% of my portfolio is covered by ETFs.

At the moment, my portfolio is still quite tech-heavy. I plan to reduce some positions, but would still like to benefit from the growth of individual stocks. I recently built up another position in $EQQQ (+0,27%) which tilts my portfolio even more towards technology. I still need to think about how I want to deal with technology in the long term.

I have also realized that my portfolio is heavily dependent on North America.

My aim in the future is to reduce the many individual positions and concentrate on my ETF and a few stocks where I believe there will be strong growth.

I also plan to invest CHF 700 per month. I still need to plan exactly how I will allocate the money.

As last time, I'm always happy to receive tips and constructive criticism! I'm still quite new to the world of investing and am learning every day.

Thanks to everyone who took the time to read my post! :D

I started investing in the stock market in June 2021. I was still on short-time work at the time, but I still managed to lay the foundation for my wealth with a savings rate of 60-70% (approx. 1,000 to 1,200 euros). Reaching the first 10,000 euros in particular was a huge challenge - it felt like an eternity.

But I kept at it: From 50,000 euros onwards, things went noticeably faster, and the step from 50,000 to 100,000 euros was almost a no-brainer. With individual stocks like $NVDA (+0,4%) at the low point in 2022 and an investment in the $EQQQ (+0,27%) as core & first $BTC (+0,9%) purchases, which I unfortunately sold at the end of 2023, I was able to achieve good returns; before that, I had invested the majority in a classic $IWDA (+0,44%) invested before I reallocated.

However, this year was a turning point: in March 2024, I sold the majority of my positions and invested just under EUR 94,000 in Bitcoin - just before the halving. It was a calculated risk, and it paid off and I look back on this year on the stock market very happily.

Looking ahead to next year is just as exciting: from March 2025, I plan to make my first Bitcoin sales and gradually add to the proceeds when good opportunities arise in shares I have already invested in. In the long term, however, I would like to hold 0.5 Bitcoin - both as a hedge and as a strategic position.

At the end of the year, my portfolio stands with a strong performance of ~ 35.1 %. I can look back on a successful year and look forward to the challenges and opportunities ahead. I wish you all a happy new year, good luck and good health for 2025. Stick to your goals - it's worth it!